In this blog, you will learn the following things:
So let’s go ahead.
What is Machine Learning?
Machine Learning is a branch of Computer Science that uses algorithms to imitate the way in which humans learn. It uses statistical methods to train algorithms and make predictions. The accuracy of these predictions improves over time.
As the amount of data increases and big data continues to grow, the demand for data scientists increases along with. Machine learning is one of the most in-demand Data Science skills, which allows data scientists to increase the accuracy of predictions of software applications, without explicitly programming them to do so.
These algorithms make use of historical data to predict output values and these insights and predictions enable businesses to make smart decisions.
Watch this video on Machine Learning by Intellipaat
Machine Learning is very important as it gives companies a view of the trends in business patterns and customer behavior. Most leading companies like Uber, Google, and Facebook focus on Machine Learning as the main focus of their operations.
There are multiple courses you can study to pursue a career in Machine Learning. So now let’s learn about them.
Read this blog by Intellipaat, to learn more about What is Machine Learning and why it may be wise to study it.
Machine Learning Courses
Machine Learning is one of the fastest-growing fields in the Computer Science industry. These days, every student wants to enhance their Machine Learning skills, as it has proven to be very beneficial in increasing the placement chances of candidates.
Here are some Machine Learning courses that you can pursue.
|Course||Type of Course||Institution / Organisation|
|Machine Learning Course||Training and Certification ||Intellipaat|
|Introduction to Machine Learning||UG Certificate||IIT Madras Through NPTEL|
|B.Sc. C.S. (Hons.) with Machine Learning and Artificial Intelligence ||Bachelor’s Degree||Karnavati University|
|B.Tech (CSE) with Artificial Intelligence and Machine Learning ||Bachelor’s Degree||Manipal Institute of Technology|
|B.Sc. I.T in Machine Learning||Bachelor’s Degree||Techno India University|
|B.Sc. Artificial Intelligence and Machine Learning||Bachelor’s Degree||Chandigarh Group of Colleges|
|B.Sc. Artificial Intelligence and Machine Learning||Bachelor’s Degree||Bharathiar University and Affiliated Colleges|
|PG Diploma in Machine Learning and Artificial Intelligence||PG Diploma||IIIT Bangalore|
|PG Certification in Data Science and Machine Learning – MNIT ||PG Certification||MNIT Jaipur through Intellipaat (In collaboration with IBM and Microsoft)|
|AI and ML PG Certification Programme||PG Certificate||BITS Pilani|
|M.Tech (ECE) with Specialization in Machine Learning||Master’s Degree||IIIT Delhi|
|MSc in Data Science and Machine Learning||Master’s Degree||Reva University|
Other than these courses mentioned above, many BTech and MTech courses offered in Computer Science also combine many Machine Learning subjects in their curriculum.
Go through these Top 40 Machine Learning Interview Questions and Answers to crack your interviews.
Now that you have seen what the different courses in Machine Learning are, let’s learn about the common subjects in the Machine Learning Course Syllabus for these courses.
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Important Subjects in Machine Learning Courses
The machine learning courses that we discussed are offered in various streams, countries, and institutes. The exact syllabus will always differ, based on the course you’re pursuing and the college or university you’re studying in, but each one of these courses focuses on the same common subjects
These subjects are designed in a way that they give an overview of Machine Learning.
Some of these subjects are-
- Programming Languages like R, Python, C++, and Java.
- Machine Learning Algorithms and Techniques
- Relation between Artificial Intelligence and Machine Learning
- Artificial Neural Networks and their applications
- Reinforcement Learning and Deep Learning
- Natural Language Processing
All these subjects are usually included in every machine learning course syllabus, be it any level of education from any university or country.
Most of these courses also include mandatory internships and live projects, during the course. These help the students to learn and understand better and get a better grasp of the subjects being taught.
Machine Learning Course Syllabus: Certifications
To understand the Machine Learning Course Syllabus for Specialization Certifications, let’s look at the syllabus of the Machine Learning Course offered by Intellipaat.
- Module 1 – Introduction to Machine Learning
- Module 2 – Supervised Learning and Linear Regression
- Module 3 – Classification and Logistic Regression
- Module 4 – Decision Tree and Random Forest
- Module 5 – Naïve Bayes and Support Vector Machine
- Module 6 – Unsupervised Learning
- Module 7 – Natural Language Processing and Text Mining
- Module 8 – Introduction to Deep Learning
- Module 9 – Time Series Analysis
The cost of such trainings and certifications can vary, depending on the course, the offering body, and the quality of the training, and the experience of the faculty, but the fees can usually range between ₹5000 to ₹20000.
Next, let’s come to the undergraduate courses and discuss them next.
Machine Learning Course Syllabus: Undergraduate
Here are some undergraduate courses in Machine Learning, that you can pursue.
UG Certification in Machine Learning Course Syllabus
After looking at the course syllabi of some UG Certifications in Machine Learning, we could conclude that the Machine Learning Course Syllabus for any UG Certification usually follows a similar pattern, with some changes here and there, depending on the institute. Let’s understand that.
|Week 1||Week 2||Week 3|
|Introduction to ML||Linear Regression||Linear Discriminant Analysis|
|Reinforcement Learning||Multivariate Regression||Linear Classification|
|Unsupervised Learning||Partial Least Squares||Logistic Regression|
|Supervised Learning||Shrinkage Methods||Project|
|Week 4||Week 5||Week 6|
|Support Vector Machines||Artificial Neural Networks||Regression Trees|
|Hinge Loss Formulation||Training and Validation||Decision Trees|
|Perceptron Learning||Parameter Estimations||Decision Trees Examples|
|Week 7||Week 8||Week 9|
|ROC Curve||Random Forests||Hidden Markov Models|
|Evaluation Measures||Bayesian Networks||Treewidth and belief|
|Ensemble Methods||Gradient Boosting||Undirected Graphical Method|
|Minimum Desc. Lgth Analysis||Naive Bayes||Variable Elimination|
|Week 10||Week 11||Week 12|
|Clustering||Expectation Maximization||Reinforcement Learning|
|Birch and Cure Algorithms||Gaussian Mixture Models||Linear Theory|
These courses are usually offered online by many reputed colleges, universities, and organizations, including highly prestigious IITs like IIT Madras.
Bachelor’s Degree in Machine Learning Course Syllabus
You can pursue a Bachelor’s degree in either a 6-semester-long course like Computer Science or an 8-semester-long course in Engineering or Technology, with a specialization in Machine Learning.
|Semester 1||Semester 2|
|English Language and|
|Programming in JAVA|
|Data Structures and Algorithms||Basic Internet Laboratory|
|Discrete Mathematics||Applied Mathematics|
|Environmental Studies||Human Resources and Rights|
|Semester 3||Semester 4|
|Programming in Python||AI and Knowledge Representation|
|Fuzzy Logic and Neural Networks||Introduction to Machine Learning|
|Design and Analysis of Algorithms||Programming in R|
|Introduction to Internet of Things||Skill Based Project Work|
|Language Elective||Major Elective|
|Semester 5||Semester 6|
|Machine Learning Techniques||Embedded Systems|
|Ethical Hacking||Natural Language Processing|
|Deep Learning||Artificial Neural Networks|
|Data Analytics Techniques||Machine Learning Live Project|
Check out this in-depth Machine Learning Tutorial by Intellipaat.
If you’re pursuing an 8-semester-long course, you might study some additional subjects like Human-Computer Interaction, Data Mining, Data Visualization, Data Modelling, Pattern Recognition, and Augmented Reality.
Machine Learning Course Syllabus: Post-Graduate
Here are some Post-Graduation courses that you can pursue.
PG Certification in Machine Learning Course Syllabus
Let’s look at the syllabus of the PG Certification in Machine Learning offered by Intellipaat, to understand the topics that are covered in the Machine Learning Course Syllabus for PG Certifications.
- Module 1 – Preparatory Classes on Python for AI & ML and Linux
- Module 2 – Git and GitHub
- Module 3 – Python with Data Science
- Module 4 – Data Wrangling with SQL
- Module 5 – Story Telling
- Module 6 – Machine Learning Models for Selection and Tuning
- Module 7 – Machine Learning & Prediction Algorithms
- Module 8 – Advanced Machine Learning
- Module 9 – Software Engineering for Data Science
- Module 10 – Data Science at Scale with PySpark
- Module 11 – Artificial Intelligence and Deep Learning with TensorFlow
- Module 12 – Natural Language Processing
- Module 13 – Image Processing and Computer Vision
- Module 14 – Deployment of Machine Learning Systems to Production
- Module 15 – Work with Large Datasets
- Module 16 – Data Visualization with Tableau
- Module 17 – Capstone Project
- Module 18 – Data Science with R
Watch this video by Intellipaat to start learning Natural Language Processing
Master’s Degree in Machine Learning Course Syllabus
After completing your undergraduate, you are eligible to pursue a 2-year-long Master’s program in Machine Learning. We analyzed the Machine Learning Course Syllabus for Master’s Programs in various reputed universities and concluded that the students usually have to study the following core and elective subjects.
- Core Subjects
- Introduction to Machine Learning
- Deep Learning or Deep Reinforcement Learning
- Probabilistic Graphical Models
- Machine Learning in Practice
- Convex Optimization
- Probability & Mathematical Statistics
- Elective Subjects
- Advanced Deep Learning
- Advanced Machine Learning: Theory and Methods
- Machine Learning with Large Datasets
- Algorithms for NLP
- Machine Learning for Text Mining
- Neural Networks for NLP
- Multimodal Machine Learning
- Graduate Artificial Intelligence
- Multimedia Databases and Data Mining
- Algorithms in the Real World
- Computer Vision and Imaging
- Regression Analysis
- Advanced Statistical Theory
- Algorithms and Complexity
- Intelligent Robotics
- Machine Learning and Intelligent Data Analysis
- Neural Computation
- Robot Vision
Book recommendations for Machine Learning Course Syllabus
Given below is a list of books that may prove to be useful to students pursuing a Bachelor’s Degree in Machine Learning.
|Data Structures||Ellis Horowitz, Sartaj Shani|
|Discrete Mathematics and its Applications||Kenneth H. Rosen|
|Python the Complete Reference||Martin C. Brown|
|Artificial Intelligence: A Systems Approach||S. Russell, P. Norvig|
|A Hundred-page Machine Learning Book||Andriya Burkov|
|Neural Networks and Fuzzy Systems||Kosko|
|Machine Learning: The art and Science of Algorithms that make sense of Data||Peter Flach |
|Artificial Intelligence: A Modern Approach||Stuart J. Russell and Peter Norwig|
The following books will be useful if you’re pursuing a Master’s Degree in Machine Learning.
|Deep Learning||Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach|
|Python Machine Learning||Sabastine Raschka and Vahid Miralili|
|Pattern Recognition and Machine Learning||Christopher Bishop|
|The Elements of Statistical Learning||Trevor Hastie, Robert Tibshirani, Jerome Friedman|
|Speech and Language Processing||Daniel Jurafsky and James H. Martin|
Now, hopefully, you’re familiar with the Machine Learning Course Syllabus at all the different levels.
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